5 papers
Error Detection for PET/CT Radiology Reports: Domain-Specific vs Large Language Models
Hermione Warr, Harry Anthony, Lilli J Freischem +3
Errors in radiology reports can adversely affect patient treatment, yet automated report quality assurance remains challenging because errors are often subtle and require domain ex…
Specialised or Generic? Tokenization Choices for Radiology Language Models
Hermione Warr, Wentian Xu, Harry Anthony +3
The vocabulary used by language models (LM) - defined by the tokenizer - plays a key role in text generation quality. However, its impact remains under-explored in radiology. In th…
IterMask3D: Unsupervised Anomaly Detection and Segmentation with Test-Time Iterative Mask Refinement in 3D Brain MR
Ziyun Liang, Xiaoqing Guo, Wentian Xu +5
Unsupervised anomaly detection and segmentation methods train a model to learn the training distribution as `normal'. In the testing phase, they identify patterns that deviate from…
You Point, I Learn: Online Adaptation of Interactive Segmentation Models for Handling Distribution Shifts in Medical Imaging
Wentian Xu, Ziyun Liang, Harry Anthony +4
Interactive segmentation uses real-time user inputs, such as mouse clicks, to iteratively refine model predictions. Although not originally designed to address distribution shifts,…
Identity-Free Deferral For Unseen Experts
Joshua Strong, Pramit Saha, Yasin Ibrahim +2
Learning to Defer (L2D) improves AI reliability in decision-critical environments by training AI to either make its own prediction or defer the decision to a human expert. A key ch…